Overview

Dataset statistics

Number of variables22
Number of observations4807
Missing cells31915
Missing cells (%)30.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory992.8 KiB
Average record size in memory211.5 B

Variable types

Numeric8
Text4
Categorical4
DateTime2
Unsupported4

Alerts

schedule_time is highly imbalanced (50.6%)Imbalance
language has 346 (7.2%) missing valuesMissing
runtime has 3598 (74.8%) missing valuesMissing
averageRuntime has 310 (6.4%) missing valuesMissing
ended has 3080 (64.1%) missing valuesMissing
officialSite has 484 (10.1%) missing valuesMissing
rating_average has 4064 (84.5%) missing valuesMissing
network has 4807 (100.0%) missing valuesMissing
dvdCountry has 4807 (100.0%) missing valuesMissing
summary has 805 (16.7%) missing valuesMissing
image has 4807 (100.0%) missing valuesMissing
webChannel has 4807 (100.0%) missing valuesMissing
id_index is uniformly distributedUniform
id_index has unique valuesUnique
id_episodes has unique valuesUnique
network is an unsupported type, check if it needs cleaning or further analysisUnsupported
dvdCountry is an unsupported type, check if it needs cleaning or further analysisUnsupported
image is an unsupported type, check if it needs cleaning or further analysisUnsupported
webChannel is an unsupported type, check if it needs cleaning or further analysisUnsupported
weight has 136 (2.8%) zerosZeros

Reproduction

Analysis started2024-12-06 23:52:25.641083
Analysis finished2024-12-06 23:52:37.082989
Duration11.44 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

id_index
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct4807
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2404
Minimum1
Maximum4807
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.1 KiB
2024-12-06T18:52:37.176919image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile241.3
Q11202.5
median2404
Q33605.5
95-th percentile4566.7
Maximum4807
Range4806
Interquartile range (IQR)2403

Descriptive statistics

Standard deviation1387.8057
Coefficient of variation (CV)0.57729023
Kurtosis-1.2
Mean2404
Median Absolute Deviation (MAD)1202
Skewness0
Sum11556028
Variance1926004.7
MonotonicityStrictly increasing
2024-12-06T18:52:37.325208image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3212 1
 
< 0.1%
3210 1
 
< 0.1%
3209 1
 
< 0.1%
3208 1
 
< 0.1%
3207 1
 
< 0.1%
3206 1
 
< 0.1%
3205 1
 
< 0.1%
3204 1
 
< 0.1%
3203 1
 
< 0.1%
Other values (4797) 4797
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4807 1
< 0.1%
4806 1
< 0.1%
4805 1
< 0.1%
4804 1
< 0.1%
4803 1
< 0.1%
4802 1
< 0.1%
4801 1
< 0.1%
4800 1
< 0.1%
4799 1
< 0.1%
4798 1
< 0.1%

id_episodes
Real number (ℝ)

UNIQUE 

Distinct4807
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2771806.2
Minimum2391730
Maximum3076658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.1 KiB
2024-12-06T18:52:37.468501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2391730
5-th percentile2693731.3
Q12732547.5
median2744513
Q32782664
95-th percentile2963061.7
Maximum3076658
Range684928
Interquartile range (IQR)50116.5

Descriptive statistics

Standard deviation77046.219
Coefficient of variation (CV)0.027796395
Kurtosis3.1474742
Mean2771806.2
Median Absolute Deviation (MAD)14723
Skewness1.7329687
Sum1.3324072 × 1010
Variance5.9361198 × 109
MonotonicityNot monotonic
2024-12-06T18:52:37.610909image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2730586 1
 
< 0.1%
2748583 1
 
< 0.1%
2748581 1
 
< 0.1%
2748580 1
 
< 0.1%
2748579 1
 
< 0.1%
2735438 1
 
< 0.1%
2826704 1
 
< 0.1%
2756123 1
 
< 0.1%
2730132 1
 
< 0.1%
2750532 1
 
< 0.1%
Other values (4797) 4797
99.8%
ValueCountFrequency (%)
2391730 1
< 0.1%
2494160 1
< 0.1%
2580338 1
< 0.1%
2580339 1
< 0.1%
2610881 1
< 0.1%
2610882 1
< 0.1%
2625941 1
< 0.1%
2633274 1
< 0.1%
2633275 1
< 0.1%
2633276 1
< 0.1%
ValueCountFrequency (%)
3076658 1
< 0.1%
3076657 1
< 0.1%
3076656 1
< 0.1%
3076655 1
< 0.1%
3076654 1
< 0.1%
3076653 1
< 0.1%
3076652 1
< 0.1%
3076651 1
< 0.1%
3076650 1
< 0.1%
3076649 1
< 0.1%

id
Real number (ℝ)

Distinct694
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63622.128
Minimum274
Maximum81261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.1 KiB
2024-12-06T18:52:37.758148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum274
5-th percentile11502
Q159822
median72561
Q374055
95-th percentile77713
Maximum81261
Range80987
Interquartile range (IQR)14233

Descriptive statistics

Standard deviation18748.631
Coefficient of variation (CV)0.29468727
Kurtosis3.2331689
Mean63622.128
Median Absolute Deviation (MAD)3724
Skewness-1.9809607
Sum3.0583157 × 108
Variance3.5151116 × 108
MonotonicityNot monotonic
2024-12-06T18:52:37.899388image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78854 100
 
2.1%
73952 38
 
0.8%
73773 36
 
0.7%
72654 36
 
0.7%
73703 36
 
0.7%
74045 34
 
0.7%
42056 33
 
0.7%
69806 32
 
0.7%
73931 30
 
0.6%
74100 28
 
0.6%
Other values (684) 4404
91.6%
ValueCountFrequency (%)
274 6
 
0.1%
703 4
 
0.1%
718 17
0.4%
729 4
 
0.1%
793 19
0.4%
802 5
 
0.1%
812 23
0.5%
875 3
 
0.1%
920 8
 
0.2%
938 6
 
0.1%
ValueCountFrequency (%)
81261 10
0.2%
81105 1
 
< 0.1%
81098 5
0.1%
81004 2
 
< 0.1%
80941 10
0.2%
80910 2
 
< 0.1%
80885 12
0.2%
80630 1
 
< 0.1%
80603 8
0.2%
80412 5
0.1%

url
Text

Distinct694
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size204.1 KiB
2024-12-06T18:52:38.117904image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length97
Median length74
Mean length52.230081
Min length35

Characters and Unicode

Total characters251070
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)1.8%

Sample

1st rowhttps://www.tvmaze.com/shows/51908/neznost
2nd rowhttps://www.tvmaze.com/shows/51908/neznost
3rd rowhttps://www.tvmaze.com/shows/51908/neznost
4th rowhttps://www.tvmaze.com/shows/51908/neznost
5th rowhttps://www.tvmaze.com/shows/51908/neznost
ValueCountFrequency (%)
https://www.tvmaze.com/shows/78854/my-beautiful-dumb-wife 100
 
2.1%
https://www.tvmaze.com/shows/73952/shanghai-picked-flowers 38
 
0.8%
https://www.tvmaze.com/shows/73773/my-boss 36
 
0.7%
https://www.tvmaze.com/shows/72654/our-interpreter 36
 
0.7%
https://www.tvmaze.com/shows/73703/just-between-us 36
 
0.7%
https://www.tvmaze.com/shows/74045/sword-and-fairy-4 34
 
0.7%
https://www.tvmaze.com/shows/42056/like-a-flowing-river 33
 
0.7%
https://www.tvmaze.com/shows/69806/scout-hero 32
 
0.7%
https://www.tvmaze.com/shows/73931/different-princess 30
 
0.6%
https://www.tvmaze.com/shows/73862/born-to-run 28
 
0.6%
Other values (684) 4404
91.6%
2024-12-06T18:52:38.644730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 24035
 
9.6%
w 20729
 
8.3%
s 19210
 
7.7%
t 19200
 
7.6%
o 14966
 
6.0%
e 13070
 
5.2%
h 12587
 
5.0%
m 11787
 
4.7%
a 11226
 
4.5%
- 10218
 
4.1%
Other values (30) 94042
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 178331
71.0%
Other Punctuation 38456
 
15.3%
Decimal Number 24065
 
9.6%
Dash Punctuation 10218
 
4.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 20729
11.6%
s 19210
10.8%
t 19200
10.8%
o 14966
 
8.4%
e 13070
 
7.3%
h 12587
 
7.1%
m 11787
 
6.6%
a 11226
 
6.3%
c 6549
 
3.7%
p 6162
 
3.5%
Other values (16) 42845
24.0%
Decimal Number
ValueCountFrequency (%)
7 4616
19.2%
6 2710
11.3%
4 2610
10.8%
3 2413
10.0%
2 2095
8.7%
5 2059
8.6%
8 1977
8.2%
9 1925
8.0%
1 1901
7.9%
0 1759
 
7.3%
Other Punctuation
ValueCountFrequency (%)
/ 24035
62.5%
. 9614
 
25.0%
: 4807
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 10218
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 178331
71.0%
Common 72739
29.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 20729
11.6%
s 19210
10.8%
t 19200
10.8%
o 14966
 
8.4%
e 13070
 
7.3%
h 12587
 
7.1%
m 11787
 
6.6%
a 11226
 
6.3%
c 6549
 
3.7%
p 6162
 
3.5%
Other values (16) 42845
24.0%
Common
ValueCountFrequency (%)
/ 24035
33.0%
- 10218
14.0%
. 9614
 
13.2%
: 4807
 
6.6%
7 4616
 
6.3%
6 2710
 
3.7%
4 2610
 
3.6%
3 2413
 
3.3%
2 2095
 
2.9%
5 2059
 
2.8%
Other values (4) 7562
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 251070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 24035
 
9.6%
w 20729
 
8.3%
s 19210
 
7.7%
t 19200
 
7.6%
o 14966
 
6.0%
e 13070
 
5.2%
h 12587
 
5.0%
m 11787
 
4.7%
a 11226
 
4.5%
- 10218
 
4.1%
Other values (30) 94042
37.5%

name
Text

Distinct692
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size204.1 KiB
2024-12-06T18:52:38.985529image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length63
Median length41
Mean length17.511338
Min length2

Characters and Unicode

Total characters84177
Distinct characters168
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)1.8%

Sample

1st rowНежность
2nd rowНежность
3rd rowНежность
4th rowНежность
5th rowНежность
ValueCountFrequency (%)
the 787
 
5.2%
of 340
 
2.3%
my 226
 
1.5%
a 181
 
1.2%
love 179
 
1.2%
news 171
 
1.1%
and 170
 
1.1%
with 130
 
0.9%
you 122
 
0.8%
world 108
 
0.7%
Other values (1415) 12655
84.0%
2024-12-06T18:52:39.415119image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10262
 
12.2%
e 7572
 
9.0%
a 5120
 
6.1%
o 4630
 
5.5%
i 4358
 
5.2%
n 4297
 
5.1%
r 3923
 
4.7%
t 3404
 
4.0%
s 3197
 
3.8%
l 2570
 
3.1%
Other values (158) 34844
41.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58525
69.5%
Uppercase Letter 14085
 
16.7%
Space Separator 10262
 
12.2%
Other Punctuation 825
 
1.0%
Decimal Number 338
 
0.4%
Dash Punctuation 112
 
0.1%
Math Symbol 19
 
< 0.1%
Other Symbol 11
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7572
12.9%
a 5120
 
8.7%
o 4630
 
7.9%
i 4358
 
7.4%
n 4297
 
7.3%
r 3923
 
6.7%
t 3404
 
5.8%
s 3197
 
5.5%
l 2570
 
4.4%
h 2327
 
4.0%
Other values (73) 17127
29.3%
Uppercase Letter
ValueCountFrequency (%)
S 1259
 
8.9%
T 1184
 
8.4%
M 903
 
6.4%
L 850
 
6.0%
B 786
 
5.6%
A 785
 
5.6%
D 680
 
4.8%
C 679
 
4.8%
W 667
 
4.7%
H 629
 
4.5%
Other values (48) 5663
40.2%
Other Punctuation
ValueCountFrequency (%)
: 296
35.9%
' 274
33.2%
& 58
 
7.0%
! 44
 
5.3%
, 43
 
5.2%
. 41
 
5.0%
/ 23
 
2.8%
@ 22
 
2.7%
? 18
 
2.2%
* 3
 
0.4%
Decimal Number
ValueCountFrequency (%)
4 65
19.2%
1 63
18.6%
2 48
14.2%
3 48
14.2%
0 35
10.4%
7 32
9.5%
9 30
8.9%
8 8
 
2.4%
5 5
 
1.5%
6 4
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 100
89.3%
12
 
10.7%
Other Symbol
ValueCountFrequency (%)
° 7
63.6%
4
36.4%
Space Separator
ValueCountFrequency (%)
10262
100.0%
Math Symbol
ValueCountFrequency (%)
+ 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 68830
81.8%
Common 11567
 
13.7%
Cyrillic 3766
 
4.5%
Greek 14
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7572
 
11.0%
a 5120
 
7.4%
o 4630
 
6.7%
i 4358
 
6.3%
n 4297
 
6.2%
r 3923
 
5.7%
t 3404
 
4.9%
s 3197
 
4.6%
l 2570
 
3.7%
h 2327
 
3.4%
Other values (67) 27432
39.9%
Cyrillic
ValueCountFrequency (%)
о 319
 
8.5%
е 314
 
8.3%
а 293
 
7.8%
и 248
 
6.6%
р 222
 
5.9%
н 217
 
5.8%
с 181
 
4.8%
т 175
 
4.6%
д 152
 
4.0%
я 141
 
3.7%
Other values (47) 1504
39.9%
Common
ValueCountFrequency (%)
10262
88.7%
: 296
 
2.6%
' 274
 
2.4%
- 100
 
0.9%
4 65
 
0.6%
1 63
 
0.5%
& 58
 
0.5%
2 48
 
0.4%
3 48
 
0.4%
! 44
 
0.4%
Other values (17) 309
 
2.7%
Greek
ValueCountFrequency (%)
ς 2
14.3%
Κ 2
14.3%
έ 2
14.3%
τ 2
14.3%
σ 2
14.3%
ω 2
14.3%
λ 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79992
95.0%
Cyrillic 3766
 
4.5%
None 400
 
0.5%
Punctuation 15
 
< 0.1%
Geometric Shapes 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10262
 
12.8%
e 7572
 
9.5%
a 5120
 
6.4%
o 4630
 
5.8%
i 4358
 
5.4%
n 4297
 
5.4%
r 3923
 
4.9%
t 3404
 
4.3%
s 3197
 
4.0%
l 2570
 
3.2%
Other values (65) 30659
38.3%
Cyrillic
ValueCountFrequency (%)
о 319
 
8.5%
е 314
 
8.3%
а 293
 
7.8%
и 248
 
6.6%
р 222
 
5.9%
н 217
 
5.8%
с 181
 
4.8%
т 175
 
4.6%
д 152
 
4.0%
я 141
 
3.7%
Other values (47) 1504
39.9%
None
ValueCountFrequency (%)
ü 67
16.8%
ı 54
13.5%
ş 44
11.0%
å 33
 
8.2%
ñ 32
 
8.0%
ö 15
 
3.8%
ä 14
 
3.5%
ø 14
 
3.5%
è 12
 
3.0%
Í 12
 
3.0%
Other values (23) 103
25.8%
Punctuation
ValueCountFrequency (%)
12
80.0%
3
 
20.0%
Geometric Shapes
ValueCountFrequency (%)
4
100.0%

type
Categorical

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size204.1 KiB
Scripted
2241 
Animation
649 
News
534 
Reality
519 
Documentary
339 
Other values (6)
525 

Length

Max length11
Median length10
Mean length7.8502184
Min length4

Characters and Unicode

Total characters37736
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowScripted
2nd rowScripted
3rd rowScripted
4th rowScripted
5th rowScripted

Common Values

ValueCountFrequency (%)
Scripted 2241
46.6%
Animation 649
 
13.5%
News 534
 
11.1%
Reality 519
 
10.8%
Documentary 339
 
7.1%
Talk Show 287
 
6.0%
Game Show 114
 
2.4%
Variety 56
 
1.2%
Sports 53
 
1.1%
Panel Show 14
 
0.3%

Length

2024-12-06T18:52:39.561409image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
scripted 2241
42.9%
animation 649
 
12.4%
news 534
 
10.2%
reality 519
 
9.9%
show 416
 
8.0%
documentary 339
 
6.5%
talk 287
 
5.5%
game 114
 
2.2%
variety 56
 
1.1%
sports 53
 
1.0%
Other values (2) 15
 
0.3%

Most occurring characters

ValueCountFrequency (%)
i 4114
10.9%
t 3857
 
10.2%
e 3817
 
10.1%
S 2710
 
7.2%
r 2690
 
7.1%
c 2580
 
6.8%
p 2294
 
6.1%
d 2242
 
5.9%
a 1979
 
5.2%
n 1651
 
4.4%
Other values (18) 9802
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 32097
85.1%
Uppercase Letter 5223
 
13.8%
Space Separator 416
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 4114
12.8%
t 3857
12.0%
e 3817
11.9%
r 2690
8.4%
c 2580
8.0%
p 2294
7.1%
d 2242
7.0%
a 1979
 
6.2%
n 1651
 
5.1%
o 1457
 
4.5%
Other values (8) 5416
16.9%
Uppercase Letter
ValueCountFrequency (%)
S 2710
51.9%
A 650
 
12.4%
N 534
 
10.2%
R 519
 
9.9%
D 339
 
6.5%
T 287
 
5.5%
G 114
 
2.2%
V 56
 
1.1%
P 14
 
0.3%
Space Separator
ValueCountFrequency (%)
416
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37320
98.9%
Common 416
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 4114
11.0%
t 3857
10.3%
e 3817
10.2%
S 2710
 
7.3%
r 2690
 
7.2%
c 2580
 
6.9%
p 2294
 
6.1%
d 2242
 
6.0%
a 1979
 
5.3%
n 1651
 
4.4%
Other values (17) 9386
25.2%
Common
ValueCountFrequency (%)
416
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 4114
10.9%
t 3857
 
10.2%
e 3817
 
10.1%
S 2710
 
7.2%
r 2690
 
7.1%
c 2580
 
6.8%
p 2294
 
6.1%
d 2242
 
5.9%
a 1979
 
5.2%
n 1651
 
4.4%
Other values (18) 9802
26.0%

language
Categorical

MISSING 

Distinct33
Distinct (%)0.7%
Missing346
Missing (%)7.2%
Memory size204.1 KiB
English
1649 
Chinese
1507 
Russian
247 
Norwegian
177 
Korean
 
106
Other values (28)
775 

Length

Max length10
Median length7
Mean length6.9939475
Min length4

Characters and Unicode

Total characters31200
Distinct characters42
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowRussian
5th rowRussian

Common Values

ValueCountFrequency (%)
English 1649
34.3%
Chinese 1507
31.4%
Russian 247
 
5.1%
Norwegian 177
 
3.7%
Korean 106
 
2.2%
Spanish 86
 
1.8%
Arabic 76
 
1.6%
Japanese 73
 
1.5%
Swedish 73
 
1.5%
Hindi 66
 
1.4%
Other values (23) 401
 
8.3%
(Missing) 346
 
7.2%

Length

2024-12-06T18:52:39.708828image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 1649
37.0%
chinese 1507
33.8%
russian 247
 
5.5%
norwegian 177
 
4.0%
korean 106
 
2.4%
spanish 86
 
1.9%
arabic 76
 
1.7%
japanese 73
 
1.6%
swedish 73
 
1.6%
hindi 66
 
1.5%
Other values (23) 401
 
9.0%

Most occurring characters

ValueCountFrequency (%)
i 4246
13.6%
n 4202
13.5%
s 4043
13.0%
e 3645
11.7%
h 3551
11.4%
g 1864
6.0%
l 1728
5.5%
E 1649
 
5.3%
C 1517
 
4.9%
a 1166
 
3.7%
Other values (32) 3589
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26739
85.7%
Uppercase Letter 4461
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 4246
15.9%
n 4202
15.7%
s 4043
15.1%
e 3645
13.6%
h 3551
13.3%
g 1864
7.0%
l 1728
6.5%
a 1166
 
4.4%
r 568
 
2.1%
u 383
 
1.4%
Other values (13) 1343
 
5.0%
Uppercase Letter
ValueCountFrequency (%)
E 1649
37.0%
C 1517
34.0%
R 247
 
5.5%
N 177
 
4.0%
S 160
 
3.6%
K 106
 
2.4%
T 106
 
2.4%
A 93
 
2.1%
H 89
 
2.0%
J 73
 
1.6%
Other values (9) 244
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 31200
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 4246
13.6%
n 4202
13.5%
s 4043
13.0%
e 3645
11.7%
h 3551
11.4%
g 1864
6.0%
l 1728
5.5%
E 1649
 
5.3%
C 1517
 
4.9%
a 1166
 
3.7%
Other values (32) 3589
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 4246
13.6%
n 4202
13.5%
s 4043
13.0%
e 3645
11.7%
h 3551
11.4%
g 1864
6.0%
l 1728
5.5%
E 1649
 
5.3%
C 1517
 
4.9%
a 1166
 
3.7%
Other values (32) 3589
11.5%

status
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size204.1 KiB
Running
2446 
Ended
1727 
To Be Determined
634 

Length

Max length16
Median length7
Mean length7.4684835
Min length5

Characters and Unicode

Total characters35901
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnded
2nd rowEnded
3rd rowEnded
4th rowEnded
5th rowEnded

Common Values

ValueCountFrequency (%)
Running 2446
50.9%
Ended 1727
35.9%
To Be Determined 634
 
13.2%

Length

2024-12-06T18:52:39.829013image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-06T18:52:39.993840image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
running 2446
40.3%
ended 1727
28.4%
to 634
 
10.4%
be 634
 
10.4%
determined 634
 
10.4%

Most occurring characters

ValueCountFrequency (%)
n 9699
27.0%
e 4263
11.9%
d 4088
11.4%
i 3080
 
8.6%
R 2446
 
6.8%
u 2446
 
6.8%
g 2446
 
6.8%
E 1727
 
4.8%
1268
 
3.5%
T 634
 
1.8%
Other values (6) 3804
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 28558
79.5%
Uppercase Letter 6075
 
16.9%
Space Separator 1268
 
3.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 9699
34.0%
e 4263
14.9%
d 4088
14.3%
i 3080
 
10.8%
u 2446
 
8.6%
g 2446
 
8.6%
o 634
 
2.2%
t 634
 
2.2%
r 634
 
2.2%
m 634
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
R 2446
40.3%
E 1727
28.4%
T 634
 
10.4%
B 634
 
10.4%
D 634
 
10.4%
Space Separator
ValueCountFrequency (%)
1268
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34633
96.5%
Common 1268
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 9699
28.0%
e 4263
12.3%
d 4088
11.8%
i 3080
 
8.9%
R 2446
 
7.1%
u 2446
 
7.1%
g 2446
 
7.1%
E 1727
 
5.0%
T 634
 
1.8%
o 634
 
1.8%
Other values (5) 3170
 
9.2%
Common
ValueCountFrequency (%)
1268
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35901
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 9699
27.0%
e 4263
11.9%
d 4088
11.4%
i 3080
 
8.6%
R 2446
 
6.8%
u 2446
 
6.8%
g 2446
 
6.8%
E 1727
 
4.8%
1268
 
3.5%
T 634
 
1.8%
Other values (6) 3804
 
10.6%

runtime
Real number (ℝ)

MISSING 

Distinct48
Distinct (%)4.0%
Missing3598
Missing (%)74.8%
Infinite0
Infinite (%)0.0%
Mean60.36311
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.1 KiB
2024-12-06T18:52:40.116539image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q120
median45
Q360
95-th percentile240
Maximum300
Range299
Interquartile range (IQR)40

Descriptive statistics

Standard deviation61.950375
Coefficient of variation (CV)1.0262953
Kurtosis4.5047931
Mean60.36311
Median Absolute Deviation (MAD)20
Skewness2.1168014
Sum72979
Variance3837.849
MonotonicityNot monotonic
2024-12-06T18:52:40.257001image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
60 290
 
6.0%
120 106
 
2.2%
30 92
 
1.9%
10 71
 
1.5%
45 71
 
1.5%
12 48
 
1.0%
240 47
 
1.0%
20 44
 
0.9%
25 40
 
0.8%
11 29
 
0.6%
Other values (38) 371
 
7.7%
(Missing) 3598
74.8%
ValueCountFrequency (%)
1 6
 
0.1%
2 14
 
0.3%
3 2
 
< 0.1%
4 2
 
< 0.1%
5 25
 
0.5%
6 2
 
< 0.1%
7 8
 
0.2%
8 24
 
0.5%
10 71
1.5%
11 29
0.6%
ValueCountFrequency (%)
300 23
 
0.5%
240 47
1.0%
210 3
 
0.1%
180 2
 
< 0.1%
159 27
 
0.6%
150 3
 
0.1%
120 106
2.2%
90 12
 
0.2%
75 11
 
0.2%
70 10
 
0.2%

averageRuntime
Real number (ℝ)

MISSING 

Distinct98
Distinct (%)2.2%
Missing310
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean44.261952
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.1 KiB
2024-12-06T18:52:40.406394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q118
median41
Q352
95-th percentile120
Maximum300
Range299
Interquartile range (IQR)34

Descriptive statistics

Standard deviation42.713201
Coefficient of variation (CV)0.96500943
Kurtosis12.357143
Mean44.261952
Median Absolute Deviation (MAD)17
Skewness3.1195624
Sum199046
Variance1824.4176
MonotonicityNot monotonic
2024-12-06T18:52:40.541020image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 576
 
12.0%
60 344
 
7.2%
15 300
 
6.2%
30 251
 
5.2%
10 220
 
4.6%
43 148
 
3.1%
120 136
 
2.8%
3 117
 
2.4%
25 104
 
2.2%
40 97
 
2.0%
Other values (88) 2204
45.8%
(Missing) 310
 
6.4%
ValueCountFrequency (%)
1 6
 
0.1%
2 44
 
0.9%
3 117
2.4%
4 5
 
0.1%
5 33
 
0.7%
6 10
 
0.2%
7 52
 
1.1%
8 43
 
0.9%
9 19
 
0.4%
10 220
4.6%
ValueCountFrequency (%)
300 23
 
0.5%
242 2
 
< 0.1%
240 69
1.4%
218 1
 
< 0.1%
194 1
 
< 0.1%
184 1
 
< 0.1%
180 30
0.6%
177 4
 
0.1%
164 3
 
0.1%
163 27
 
0.6%
Distinct463
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size204.1 KiB
Minimum1944-01-20 00:00:00
Maximum2024-02-09 00:00:00
2024-12-06T18:52:40.678002image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:40.822186image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ended
Date

MISSING 

Distinct76
Distinct (%)4.4%
Missing3080
Missing (%)64.1%
Memory size204.1 KiB
Minimum2024-01-01 00:00:00
Maximum2024-11-09 00:00:00
2024-12-06T18:52:40.959937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:41.105922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

officialSite
Text

MISSING 

Distinct612
Distinct (%)14.2%
Missing484
Missing (%)10.1%
Memory size204.1 KiB
2024-12-06T18:52:41.308760image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length250
Median length104
Mean length52.1418
Min length16

Characters and Unicode

Total characters225409
Distinct characters96
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)1.7%

Sample

1st rowhttps://www.ivi.ru/watch/nezhnost
2nd rowhttps://www.ivi.ru/watch/nezhnost
3rd rowhttps://www.ivi.ru/watch/nezhnost
4th rowhttps://www.ivi.ru/watch/nezhnost
5th rowhttps://www.ivi.ru/watch/nezhnost
ValueCountFrequency (%)
https://flameserial.ru/season/12949 100
 
2.3%
https://abcnews.go.com/live 92
 
2.1%
https://v.qq.com/x/cover/mzc002005kvupzf.html 38
 
0.9%
https://v.youku.com/v_nextstage/id_ebdb60223f3e44c7aadf.html?spm=a2h0c.8166622.phonesokuprogram_1.dtitle 36
 
0.8%
https://w.mgtv.com/h/600824/20020678.html 36
 
0.8%
https://w.mgtv.com/b/610526/20301892.html?fpa=se&lastp=so_result 36
 
0.8%
https://www.iq.com/album/sword-and-fairy-4-2024-13ndvpx4xm1?lang=en_us 34
 
0.8%
https://v.qq.com/x/cover/mzc00200syv5tor.html 33
 
0.8%
https://www.iq.com/album/scout-hero-2023-1oipynj6bzh?lang=en_us 32
 
0.7%
https://v.youku.com/v_show/id_xnji5odc3mdm1mg==.html 30
 
0.7%
Other values (602) 3856
89.2%
2024-12-06T18:52:41.686195image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 18532
 
8.2%
t 15704
 
7.0%
s 10856
 
4.8%
o 10550
 
4.7%
. 10266
 
4.6%
e 10060
 
4.5%
w 8907
 
4.0%
h 8570
 
3.8%
m 8467
 
3.8%
c 7975
 
3.5%
Other values (86) 115522
51.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 149708
66.4%
Other Punctuation 35313
 
15.7%
Decimal Number 26002
 
11.5%
Uppercase Letter 7349
 
3.3%
Dash Punctuation 4468
 
2.0%
Connector Punctuation 1461
 
0.6%
Math Symbol 1056
 
0.5%
Other Letter 52
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 15704
 
10.5%
s 10856
 
7.3%
o 10550
 
7.0%
e 10060
 
6.7%
w 8907
 
5.9%
h 8570
 
5.7%
m 8467
 
5.7%
c 7975
 
5.3%
a 7719
 
5.2%
p 7399
 
4.9%
Other values (27) 53501
35.7%
Uppercase Letter
ValueCountFrequency (%)
B 619
 
8.4%
E 604
 
8.2%
P 589
 
8.0%
A 448
 
6.1%
N 388
 
5.3%
T 381
 
5.2%
L 343
 
4.7%
M 324
 
4.4%
S 311
 
4.2%
O 294
 
4.0%
Other values (18) 3048
41.5%
Decimal Number
ValueCountFrequency (%)
0 5491
21.1%
2 3675
14.1%
1 2832
10.9%
4 2442
9.4%
8 2314
8.9%
6 2179
 
8.4%
3 1926
 
7.4%
9 1921
 
7.4%
5 1768
 
6.8%
7 1454
 
5.6%
Other Punctuation
ValueCountFrequency (%)
/ 18532
52.5%
. 10266
29.1%
: 4335
 
12.3%
% 1260
 
3.6%
? 587
 
1.7%
& 225
 
0.6%
@ 95
 
0.3%
, 8
 
< 0.1%
# 5
 
< 0.1%
Other Letter
ValueCountFrequency (%)
9
17.3%
9
17.3%
9
17.3%
9
17.3%
4
7.7%
4
7.7%
4
7.7%
4
7.7%
Math Symbol
ValueCountFrequency (%)
= 1044
98.9%
~ 12
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 4468
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1461
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 157036
69.7%
Common 68300
30.3%
Han 52
 
< 0.1%
Cyrillic 21
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 15704
 
10.0%
s 10856
 
6.9%
o 10550
 
6.7%
e 10060
 
6.4%
w 8907
 
5.7%
h 8570
 
5.5%
m 8467
 
5.4%
c 7975
 
5.1%
a 7719
 
4.9%
p 7399
 
4.7%
Other values (42) 60829
38.7%
Common
ValueCountFrequency (%)
/ 18532
27.1%
. 10266
15.0%
0 5491
 
8.0%
- 4468
 
6.5%
: 4335
 
6.3%
2 3675
 
5.4%
1 2832
 
4.1%
4 2442
 
3.6%
8 2314
 
3.4%
6 2179
 
3.2%
Other values (13) 11766
17.2%
Cyrillic
ValueCountFrequency (%)
а 5
23.8%
н 3
14.3%
р 2
 
9.5%
к 2
 
9.5%
У 1
 
4.8%
в 1
 
4.8%
і 1
 
4.8%
и 1
 
4.8%
д 1
 
4.8%
м 1
 
4.8%
Other values (3) 3
14.3%
Han
ValueCountFrequency (%)
9
17.3%
9
17.3%
9
17.3%
9
17.3%
4
7.7%
4
7.7%
4
7.7%
4
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 225336
> 99.9%
CJK 52
 
< 0.1%
Cyrillic 21
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 18532
 
8.2%
t 15704
 
7.0%
s 10856
 
4.8%
o 10550
 
4.7%
. 10266
 
4.6%
e 10060
 
4.5%
w 8907
 
4.0%
h 8570
 
3.8%
m 8467
 
3.8%
c 7975
 
3.5%
Other values (65) 115449
51.2%
CJK
ValueCountFrequency (%)
9
17.3%
9
17.3%
9
17.3%
9
17.3%
4
7.7%
4
7.7%
4
7.7%
4
7.7%
Cyrillic
ValueCountFrequency (%)
а 5
23.8%
н 3
14.3%
р 2
 
9.5%
к 2
 
9.5%
У 1
 
4.8%
в 1
 
4.8%
і 1
 
4.8%
и 1
 
4.8%
д 1
 
4.8%
м 1
 
4.8%
Other values (3) 3
14.3%

schedule_time
Categorical

IMBALANCE 

Distinct48
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size204.1 KiB
2777 
12:00
440 
10:00
 
265
18:00
 
237
20:00
 
96
Other values (43)
992 

Length

Max length5
Median length0
Mean length2.1115041
Min length0

Characters and Unicode

Total characters10150
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
2777
57.8%
12:00 440
 
9.2%
10:00 265
 
5.5%
18:00 237
 
4.9%
20:00 96
 
2.0%
21:00 81
 
1.7%
13:00 78
 
1.6%
19:00 76
 
1.6%
06:00 74
 
1.5%
07:00 72
 
1.5%
Other values (38) 611
 
12.7%

Length

2024-12-06T18:52:41.827760image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00 440
21.7%
10:00 265
13.1%
18:00 237
11.7%
20:00 96
 
4.7%
21:00 81
 
4.0%
13:00 78
 
3.8%
19:00 76
 
3.7%
06:00 74
 
3.6%
07:00 72
 
3.5%
00:00 65
 
3.2%
Other values (37) 546
26.9%

Most occurring characters

ValueCountFrequency (%)
0 4445
43.8%
: 2030
20.0%
1 1540
 
15.2%
2 878
 
8.7%
3 330
 
3.3%
8 247
 
2.4%
9 242
 
2.4%
7 157
 
1.5%
6 142
 
1.4%
5 109
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8120
80.0%
Other Punctuation 2030
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4445
54.7%
1 1540
 
19.0%
2 878
 
10.8%
3 330
 
4.1%
8 247
 
3.0%
9 242
 
3.0%
7 157
 
1.9%
6 142
 
1.7%
5 109
 
1.3%
4 30
 
0.4%
Other Punctuation
ValueCountFrequency (%)
: 2030
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4445
43.8%
: 2030
20.0%
1 1540
 
15.2%
2 878
 
8.7%
3 330
 
3.3%
8 247
 
2.4%
9 242
 
2.4%
7 157
 
1.5%
6 142
 
1.4%
5 109
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4445
43.8%
: 2030
20.0%
1 1540
 
15.2%
2 878
 
8.7%
3 330
 
3.3%
8 247
 
2.4%
9 242
 
2.4%
7 157
 
1.5%
6 142
 
1.4%
5 109
 
1.1%

rating_average
Real number (ℝ)

MISSING 

Distinct41
Distinct (%)5.5%
Missing4064
Missing (%)84.5%
Infinite0
Infinite (%)0.0%
Mean6.4503365
Minimum1
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.1 KiB
2024-12-06T18:52:42.185764image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.3
Q16
median6.8
Q37.3
95-th percentile7.9
Maximum8.2
Range7.2
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.3639355
Coefficient of variation (CV)0.21145183
Kurtosis3.991947
Mean6.4503365
Median Absolute Deviation (MAD)0.6
Skewness-1.8154452
Sum4792.6
Variance1.86032
MonotonicityNot monotonic
2024-12-06T18:52:42.313946image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
7 66
 
1.4%
7.1 43
 
0.9%
7.3 42
 
0.9%
7.4 42
 
0.9%
7.8 41
 
0.9%
6.7 34
 
0.7%
7.2 32
 
0.7%
6.8 30
 
0.6%
7.7 27
 
0.6%
6.3 27
 
0.6%
Other values (31) 359
 
7.5%
(Missing) 4064
84.5%
ValueCountFrequency (%)
1 7
 
0.1%
1.3 8
 
0.2%
2.1 10
0.2%
2.2 2
 
< 0.1%
4.1 6
 
0.1%
4.3 20
0.4%
4.4 19
0.4%
4.7 1
 
< 0.1%
4.8 24
0.5%
5 7
 
0.1%
ValueCountFrequency (%)
8.2 3
 
0.1%
8.1 4
 
0.1%
8 26
0.5%
7.9 11
 
0.2%
7.8 41
0.9%
7.7 27
0.6%
7.6 5
 
0.1%
7.5 12
 
0.2%
7.4 42
0.9%
7.3 42
0.9%

weight
Real number (ℝ)

ZEROS 

Distinct96
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.206574
Minimum0
Maximum100
Zeros136
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size204.1 KiB
2024-12-06T18:52:42.455714image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median23
Q354
95-th percentile95
Maximum100
Range100
Interquartile range (IQR)48

Descriptive statistics

Standard deviation30.796696
Coefficient of variation (CV)0.95622393
Kurtosis-0.65621002
Mean32.206574
Median Absolute Deviation (MAD)17
Skewness0.84291071
Sum154817
Variance948.43651
MonotonicityNot monotonic
2024-12-06T18:52:42.595812image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 687
 
14.3%
8 325
 
6.8%
4 218
 
4.5%
23 185
 
3.8%
12 176
 
3.7%
3 169
 
3.5%
9 147
 
3.1%
0 136
 
2.8%
18 123
 
2.6%
1 118
 
2.5%
Other values (86) 2523
52.5%
ValueCountFrequency (%)
0 136
 
2.8%
1 118
 
2.5%
2 18
 
0.4%
3 169
 
3.5%
4 218
 
4.5%
5 18
 
0.4%
6 687
14.3%
7 69
 
1.4%
8 325
6.8%
9 147
 
3.1%
ValueCountFrequency (%)
100 5
 
0.1%
99 31
0.6%
98 54
1.1%
97 76
1.6%
96 52
1.1%
95 33
0.7%
94 48
1.0%
93 13
 
0.3%
92 34
0.7%
90 37
0.8%

network
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4807
Missing (%)100.0%
Memory size204.1 KiB

dvdCountry
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4807
Missing (%)100.0%
Memory size204.1 KiB

summary
Text

MISSING 

Distinct597
Distinct (%)14.9%
Missing805
Missing (%)16.7%
Memory size204.1 KiB
2024-12-06T18:52:42.903639image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length1931
Median length652
Mean length384.08396
Min length50

Characters and Unicode

Total characters1537104
Distinct characters301
Distinct categories14 ?
Distinct scripts6 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)1.8%

Sample

1st row<p>30-year-old Sasha is a serial loser trying with all his might to become a successful business coach. Fate leads him to billionaire Oleg Kalugin, who decides to hire the resilient dreamer as a coach. However, Kalugin does not need advice on business, which the guy knows nothing about, but the secret of his ability to sincerely enjoy life despite poverty and other problems. From this moment, drastic changes begin in Sasha's life, which show the real price of success. Step by step, he moves further and further away from happiness, plunging into the world of deception, betrayal, hatred and really big, but dirty money.</p>
2nd row<p>30-year-old Sasha is a serial loser trying with all his might to become a successful business coach. Fate leads him to billionaire Oleg Kalugin, who decides to hire the resilient dreamer as a coach. However, Kalugin does not need advice on business, which the guy knows nothing about, but the secret of his ability to sincerely enjoy life despite poverty and other problems. From this moment, drastic changes begin in Sasha's life, which show the real price of success. Step by step, he moves further and further away from happiness, plunging into the world of deception, betrayal, hatred and really big, but dirty money.</p>
3rd row<p>30-year-old Sasha is a serial loser trying with all his might to become a successful business coach. Fate leads him to billionaire Oleg Kalugin, who decides to hire the resilient dreamer as a coach. However, Kalugin does not need advice on business, which the guy knows nothing about, but the secret of his ability to sincerely enjoy life despite poverty and other problems. From this moment, drastic changes begin in Sasha's life, which show the real price of success. Step by step, he moves further and further away from happiness, plunging into the world of deception, betrayal, hatred and really big, but dirty money.</p>
4th row<p>30-year-old Sasha is a serial loser trying with all his might to become a successful business coach. Fate leads him to billionaire Oleg Kalugin, who decides to hire the resilient dreamer as a coach. However, Kalugin does not need advice on business, which the guy knows nothing about, but the secret of his ability to sincerely enjoy life despite poverty and other problems. From this moment, drastic changes begin in Sasha's life, which show the real price of success. Step by step, he moves further and further away from happiness, plunging into the world of deception, betrayal, hatred and really big, but dirty money.</p>
5th row<p>30-year-old Sasha is a serial loser trying with all his might to become a successful business coach. Fate leads him to billionaire Oleg Kalugin, who decides to hire the resilient dreamer as a coach. However, Kalugin does not need advice on business, which the guy knows nothing about, but the secret of his ability to sincerely enjoy life despite poverty and other problems. From this moment, drastic changes begin in Sasha's life, which show the real price of success. Step by step, he moves further and further away from happiness, plunging into the world of deception, betrayal, hatred and really big, but dirty money.</p>
ValueCountFrequency (%)
the 15214
 
6.0%
and 9102
 
3.6%
to 7201
 
2.8%
of 7172
 
2.8%
a 7085
 
2.8%
in 4563
 
1.8%
is 2757
 
1.1%
with 2652
 
1.0%
her 2567
 
1.0%
his 2329
 
0.9%
Other values (8298) 192594
76.1%
2024-12-06T18:52:43.364869image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
248875
16.2%
e 146959
 
9.6%
t 96903
 
6.3%
a 96717
 
6.3%
n 89864
 
5.8%
i 89064
 
5.8%
o 85297
 
5.5%
s 77568
 
5.0%
r 73071
 
4.8%
h 64963
 
4.2%
Other values (291) 467823
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1172810
76.3%
Space Separator 249272
 
16.2%
Uppercase Letter 43187
 
2.8%
Other Punctuation 40357
 
2.6%
Math Symbol 21951
 
1.4%
Decimal Number 3276
 
0.2%
Dash Punctuation 3131
 
0.2%
Other Letter 2223
 
0.1%
Close Punctuation 408
 
< 0.1%
Open Punctuation 408
 
< 0.1%
Other values (4) 81
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
112
 
5.0%
70
 
3.1%
63
 
2.8%
56
 
2.5%
56
 
2.5%
49
 
2.2%
42
 
1.9%
42
 
1.9%
35
 
1.6%
35
 
1.6%
Other values (165) 1663
74.8%
Lowercase Letter
ValueCountFrequency (%)
e 146959
12.5%
t 96903
 
8.3%
a 96717
 
8.2%
n 89864
 
7.7%
i 89064
 
7.6%
o 85297
 
7.3%
s 77568
 
6.6%
r 73071
 
6.2%
h 64963
 
5.5%
l 47678
 
4.1%
Other values (44) 304726
26.0%
Uppercase Letter
ValueCountFrequency (%)
T 4041
 
9.4%
S 3844
 
8.9%
A 3070
 
7.1%
C 2518
 
5.8%
H 2494
 
5.8%
L 2281
 
5.3%
Y 2209
 
5.1%
M 2120
 
4.9%
B 1798
 
4.2%
J 1687
 
3.9%
Other values (18) 17125
39.7%
Other Punctuation
ValueCountFrequency (%)
, 15817
39.2%
. 11866
29.4%
/ 5638
 
14.0%
' 2977
 
7.4%
" 2169
 
5.4%
! 515
 
1.3%
? 488
 
1.2%
: 304
 
0.8%
; 267
 
0.7%
140
 
0.3%
Other values (6) 176
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 888
27.1%
1 714
21.8%
2 522
15.9%
9 403
12.3%
3 203
 
6.2%
5 129
 
3.9%
4 128
 
3.9%
8 123
 
3.8%
7 92
 
2.8%
6 74
 
2.3%
Math Symbol
ValueCountFrequency (%)
> 10975
50.0%
< 10975
50.0%
+ 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 2851
91.1%
146
 
4.7%
134
 
4.3%
Currency Symbol
ValueCountFrequency (%)
$ 22
55.0%
£ 12
30.0%
6
 
15.0%
Space Separator
ValueCountFrequency (%)
248875
99.8%
  397
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 390
95.6%
] 18
 
4.4%
Open Punctuation
ValueCountFrequency (%)
( 390
95.6%
[ 18
 
4.4%
Initial Punctuation
ValueCountFrequency (%)
19
100.0%
Modifier Letter
ValueCountFrequency (%)
11
100.0%
Other Symbol
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1215959
79.1%
Common 318884
 
20.7%
Han 2179
 
0.1%
Katakana 44
 
< 0.1%
Cyrillic 24
 
< 0.1%
Greek 14
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
112
 
5.1%
70
 
3.2%
63
 
2.9%
56
 
2.6%
56
 
2.6%
49
 
2.2%
42
 
1.9%
42
 
1.9%
35
 
1.6%
35
 
1.6%
Other values (161) 1619
74.3%
Latin
ValueCountFrequency (%)
e 146959
12.1%
t 96903
 
8.0%
a 96717
 
8.0%
n 89864
 
7.4%
i 89064
 
7.3%
o 85297
 
7.0%
s 77568
 
6.4%
r 73071
 
6.0%
h 64963
 
5.3%
l 47678
 
3.9%
Other values (58) 347875
28.6%
Common
ValueCountFrequency (%)
248875
78.0%
, 15817
 
5.0%
. 11866
 
3.7%
> 10975
 
3.4%
< 10975
 
3.4%
/ 5638
 
1.8%
' 2977
 
0.9%
- 2851
 
0.9%
" 2169
 
0.7%
0 888
 
0.3%
Other values (34) 5853
 
1.8%
Cyrillic
ValueCountFrequency (%)
и 6
25.0%
А 3
12.5%
т 3
12.5%
п 3
12.5%
о 3
12.5%
д 3
12.5%
н 3
12.5%
Greek
ValueCountFrequency (%)
έ 2
14.3%
ς 2
14.3%
σ 2
14.3%
τ 2
14.3%
ω 2
14.3%
λ 2
14.3%
Κ 2
14.3%
Katakana
ValueCountFrequency (%)
11
25.0%
11
25.0%
11
25.0%
11
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1533622
99.8%
CJK 2179
 
0.1%
None 836
 
0.1%
Punctuation 371
 
< 0.1%
Katakana 55
 
< 0.1%
Cyrillic 24
 
< 0.1%
Dingbats 11
 
< 0.1%
Currency Symbols 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
248875
16.2%
e 146959
 
9.6%
t 96903
 
6.3%
a 96717
 
6.3%
n 89864
 
5.9%
i 89064
 
5.8%
o 85297
 
5.6%
s 77568
 
5.1%
r 73071
 
4.8%
h 64963
 
4.2%
Other values (75) 464341
30.3%
None
ValueCountFrequency (%)
  397
47.5%
140
 
16.7%
ä 57
 
6.8%
å 44
 
5.3%
ö 34
 
4.1%
ó 24
 
2.9%
é 23
 
2.8%
á 21
 
2.5%
14
 
1.7%
ç 12
 
1.4%
Other values (17) 70
 
8.4%
Punctuation
ValueCountFrequency (%)
146
39.4%
134
36.1%
72
19.4%
19
 
5.1%
CJK
ValueCountFrequency (%)
112
 
5.1%
70
 
3.2%
63
 
2.9%
56
 
2.6%
56
 
2.6%
49
 
2.2%
42
 
1.9%
42
 
1.9%
35
 
1.6%
35
 
1.6%
Other values (161) 1619
74.3%
Katakana
ValueCountFrequency (%)
11
20.0%
11
20.0%
11
20.0%
11
20.0%
11
20.0%
Dingbats
ValueCountFrequency (%)
11
100.0%
Cyrillic
ValueCountFrequency (%)
и 6
25.0%
А 3
12.5%
т 3
12.5%
п 3
12.5%
о 3
12.5%
д 3
12.5%
н 3
12.5%
Currency Symbols
ValueCountFrequency (%)
6
100.0%

updated
Real number (ℝ)

Distinct694
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7171231 × 109
Minimum1.6983432 × 109
Maximum1.7335261 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size204.1 KiB
2024-12-06T18:52:43.519887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.6983432 × 109
5-th percentile1.7048212 × 109
Q11.7067971 × 109
median1.7151157 × 109
Q31.7271965 × 109
95-th percentile1.7333958 × 109
Maximum1.7335261 × 109
Range35182890
Interquartile range (IQR)20399419

Descriptive statistics

Standard deviation10491541
Coefficient of variation (CV)0.0061099524
Kurtosis-1.4409164
Mean1.7171231 × 109
Median Absolute Deviation (MAD)8957840
Skewness0.30291008
Sum8.254211 × 1012
Variance1.1007243 × 1014
MonotonicityNot monotonic
2024-12-06T18:52:43.666431image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1723133542 100
 
2.1%
1707133992 38
 
0.8%
1706282249 36
 
0.7%
1706192291 36
 
0.7%
1705897985 36
 
0.7%
1706797142 34
 
0.7%
1711774278 33
 
0.7%
1706339205 32
 
0.7%
1706957455 30
 
0.6%
1706797129 28
 
0.6%
Other values (684) 4404
91.6%
ValueCountFrequency (%)
1698343176 4
0.1%
1699173762 4
0.1%
1699196321 3
0.1%
1700067953 1
 
< 0.1%
1701776723 7
0.1%
1703096478 4
0.1%
1703320852 7
0.1%
1703404987 3
0.1%
1703852377 4
0.1%
1703934794 4
0.1%
ValueCountFrequency (%)
1733526066 2
 
< 0.1%
1733516400 2
 
< 0.1%
1733514005 4
 
0.1%
1733511962 5
 
0.1%
1733511391 17
0.4%
1733508377 22
0.5%
1733507443 4
 
0.1%
1733507212 23
0.5%
1733506245 4
 
0.1%
1733506170 4
 
0.1%

image
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4807
Missing (%)100.0%
Memory size204.1 KiB

webChannel
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4807
Missing (%)100.0%
Memory size204.1 KiB

Interactions

2024-12-06T18:52:34.936861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:27.379745image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:28.393212image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:29.462792image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:30.760797image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:31.727971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:32.788454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:33.839411image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:35.069288image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:27.500107image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:28.554479image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:29.809267image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:30.880669image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:31.878912image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:32.918187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:33.967089image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:35.211087image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:27.645334image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:28.695204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:29.945268image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:31.000119image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:32.007465image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:33.056726image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:34.114395image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:35.351423image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:27.781526image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:28.830453image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:30.081348image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:31.129621image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:32.144244image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:33.192046image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2024-12-06T18:52:27.901328image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:28.955263image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:30.215787image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:31.250954image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:32.275355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:33.300520image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:34.387579image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:35.614488image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:28.023971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:29.080953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:30.338342image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:31.368660image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:32.397029image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:33.418683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:34.519881image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:35.755174image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:28.148945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:29.209644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:30.473407image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:31.481149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:32.527139image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:33.558223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:34.661573image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:35.882119image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:28.265997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:29.330957image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:30.605268image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:31.597920image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:32.653785image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:33.697218image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-12-06T18:52:34.803620image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Missing values

2024-12-06T18:52:36.102156image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-06T18:52:36.721356image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-06T18:52:36.959744image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id_indexid_episodesidurlnametypelanguagestatusruntimeaverageRuntimepremieredendedofficialSiteschedule_timerating_averageweightnetworkdvdCountrysummaryupdatedimagewebChannel
01273058651908https://www.tvmaze.com/shows/51908/neznostНежностьScriptedRussianEndedNone192020-11-122024-01-01https://www.ivi.ru/watch/nezhnostNone11NoneNoneNone1704215354NaNNaN
12273058751908https://www.tvmaze.com/shows/51908/neznostНежностьScriptedRussianEndedNone192020-11-122024-01-01https://www.ivi.ru/watch/nezhnostNone11NoneNoneNone1704215354NaNNaN
23273058851908https://www.tvmaze.com/shows/51908/neznostНежностьScriptedRussianEndedNone192020-11-122024-01-01https://www.ivi.ru/watch/nezhnostNone11NoneNoneNone1704215354NaNNaN
34273058951908https://www.tvmaze.com/shows/51908/neznostНежностьScriptedRussianEndedNone192020-11-122024-01-01https://www.ivi.ru/watch/nezhnostNone11NoneNoneNone1704215354NaNNaN
45273059051908https://www.tvmaze.com/shows/51908/neznostНежностьScriptedRussianEndedNone192020-11-122024-01-01https://www.ivi.ru/watch/nezhnostNone11NoneNoneNone1704215354NaNNaN
56273059151908https://www.tvmaze.com/shows/51908/neznostНежностьScriptedRussianEndedNone192020-11-122024-01-01https://www.ivi.ru/watch/nezhnostNone11NoneNoneNone1704215354NaNNaN
67273059251908https://www.tvmaze.com/shows/51908/neznostНежностьScriptedRussianEndedNone192020-11-122024-01-01https://www.ivi.ru/watch/nezhnostNone11NoneNoneNone1704215354NaNNaN
78273059351908https://www.tvmaze.com/shows/51908/neznostНежностьScriptedRussianEndedNone192020-11-122024-01-01https://www.ivi.ru/watch/nezhnostNone11NoneNoneNone1704215354NaNNaN
89273059451908https://www.tvmaze.com/shows/51908/neznostНежностьScriptedRussianEndedNone192020-11-122024-01-01https://www.ivi.ru/watch/nezhnostNone11NoneNoneNone1704215354NaNNaN
910273059551908https://www.tvmaze.com/shows/51908/neznostНежностьScriptedRussianEndedNone192020-11-122024-01-01https://www.ivi.ru/watch/nezhnostNone11NoneNoneNone1704215354NaNNaN
id_indexid_episodesidurlnametypelanguagestatusruntimeaverageRuntimepremieredendedofficialSiteschedule_timerating_averageweightnetworkdvdCountrysummaryupdatedimagewebChannel
47974798292052673778https://www.tvmaze.com/shows/73778/dromkakar-utomlandsDrömkåkar utomlandsRealitySwedishTo Be DeterminedNone452024-01-10Nonehttps://www.tv4play.se/program/9e5573b08abbda332d28/dromkakar-utomlandsNone3NoneNone<p>For two years, we get to follow Swedes who build and renovate the houses they dreamed of, abroad. But the journey to the dream home is not always straight.</p>1718874160NaNNaN
47984799276104256531https://www.tvmaze.com/shows/56531/dimension-20Dimension 20Game ShowEnglishRunningNone1082018-09-12Nonehttps://www.dropout.tv/dimension-2019:00None87NoneNone<p>Heed the call of adventure and enter <b>Dimension 20</b> where Game Master Brennan Lee Mulligan, joined by comedians and pro gamers, blends comedy with tabletop RPGs.</p>1733506170NaNNaN
47994800279453375261https://www.tvmaze.com/shows/75261/the-daily-report-with-john-dickersonThe Daily Report with John DickersonNewsNoneRunning60602022-09-06Nonehttps://www.cbsnews.com/prime-time-with-john-dickerson/18:00None6NoneNone<p>John Dickerson provides in-depth reporting on news stories and interviews newsmakers.</p>1722688947NaNNaN
48004801283304876215https://www.tvmaze.com/shows/76215/abc-prime-with-linsey-davisABC Prime with Linsey DavisNewsEnglishRunningNone902020-02-17Nonehttps://abcnews.go.com/Live19:00None6NoneNone<p>Providing prime-time context and analysis of the day's top stories, as well as in-depth reporting and storytelling from around the country and the globe.</p>1728235929NaNNaN
48014802275045773963https://www.tvmaze.com/shows/73963/camilla-hamids-bakresa-marockoCamilla Hamids bakresa: MarockoRealitySwedishRunningNoneNone2024-01-24Nonehttps://www.svtplay.se/camilla-hamids-bakresa-marocko02:00None3NoneNone<p>Come along to Camilla's Moroccan family where she gets to learn about the Moroccan baking culture together to understand more about where she belongs. Camilla has always felt too Swedish in Morocco and too Moroccan in Sweden and never really felt 100% at home anywhere. With this program, she hopes not only to offer new exciting baking pleasure, but also understanding and recognition.</p>1706117901NaNNaN
48024803294163949496https://www.tvmaze.com/shows/49496/trafficked-with-mariana-van-zellerTrafficked with Mariana van ZellerDocumentaryEnglishTo Be Determined60622020-12-02Nonehttps://www.nationalgeographic.com/tv/shows/trafficked-with-mariana-van-zeller21:007.890NaNNone<p>Armed with National Geographic's trademark inside access, <b>Trafficked with Mariana van Zeller</b> takes viewers on a journey inside the most dangerous black markets on the planet. Each investigation in the eight-part series embeds with Peabody and duPont Award-winning journalist Mariana van Zeller as she explores the complex and often violent inner workings of a smuggling network. While she dives deeper and deeper into these underworlds, Mariana reveals - with characteristic boldness and empathy - that the people operating these trafficking rings are often a lot more like us than we realize.</p>1720942651NaNNaN
48034804273235054476https://www.tvmaze.com/shows/54476/alle-elsker-davidAlle Elsker DavidRealityNorwegianRunningNone222021-03-08Nonehttps://play.tv2.no/programmer/underholdning/alle-elsker-davidNone11NoneNone<p>We follow manager David Eriksen and his charming but untraditional family. In David's new company, the pace is high and the drop is great.</p>1732468760NaNNaN
48044805276508473167https://www.tvmaze.com/shows/73167/disasterinas-my-drag-is-validDisasterina's My Drag Is ValidTalk ShowEnglishRunningNone242023-10-25Nonehttps://www.outtvgo.com/details/TV_SHOW/collection/6339796989112/disasterinas-my-drag-is-valid00:00None6NoneNone<p>Disasterina, star of Sado Psychiatrist and The Boulet Brothers' Dragula, interviews a variety of drag artists to showcase the different styles of drag in performance, looks, and personalities. From seasoned underground fan favorites to the lesser known newbies, Disasterina and her talented guests prove that ALL drag is valid.</p>1731568633NaNNaN
48054806284803276581https://www.tvmaze.com/shows/76581/fox-news-nightFox News @ NightNewsEnglishRunning60602017-10-30Nonehttps://www.foxnews.com/shows/fox-news-night23:00None6NaNNone<p><b>Fox News @ Night</b> is a live hour of hard news and analysis of the most compelling stories from Washington and across the country.</p>1716912888NaNNaN
480648072751926718https://www.tvmaze.com/shows/718/the-tonight-show-starring-jimmy-fallonThe Tonight Show Starring Jimmy FallonTalk ShowEnglishRunning60602014-02-17Nonehttp://www.nbc.com/the-tonight-show23:354.499NaNNone<p>Emmy Award and Grammy Award winner Jimmy Fallon brought NBC's "The Tonight Show" back to its New York origins when he launched <b>The Tonight Show Starring Jimmy Fallon </b>from Rockefeller Center. Fallon puts his own stamp on the storied NBC late-night franchise with his unique comedic wit, on-point pop culture awareness, welcoming style and impeccable taste in music with the award-winning house band, The Roots.</p>1733511391NaNNaN